Data-Driven Strategies for Enhancing Sustainable Livelihoods: An Artificial Neural Network Based Approach to Policy Optimization

Abstract

Sustainable livelihoods form part of the core of inclusive development, yet traditional models of analysis normally cannot fully capture their intrinsic complexity, non-linearity, and dynamic interdependence. This paper presents a novel computational framework that integrates Artificial Neural Networks (ANN), Fuzzy Logic, and Mathematical Topology for modeling and optimization of the determinants of sustainable livelihoods. The study develops the framework of the Generalized Under-Achievement Index-GUAI-ANN through the implementation of a supervised learning and optimization algorithm which captures the multi-dimensional interactions among livelihood capitals, namely, human, social, physical, natural, and financial. It adopts feature selection, dimensionality reduction, and sensitivity analyses to enhance model interpretability and raise its policy relevance. The results show that the GUAI–ANN model identifies underachievement domains that are critical and propounds data-driven strategies for optimizing livelihood outcomes. Conjoining technical precision with developmental application, this approach illustrates how an AI methodology can be pursued to reach congruence with sustainable livelihood policy design.